{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in range(1880, 2011):\n",
    "    path  = './babynames/yob%d.txt'%year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "# 合并\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1690784 entries, 0 to 1690783\n",
      "Data columns (total 4 columns):\n",
      "name      1690784 non-null object\n",
      "sex       1690784 non-null object\n",
      "births    1690784 non-null int64\n",
      "year      1690784 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 51.6+ MB\n"
     ]
    }
   ],
   "source": [
    "names.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year\n",
       "0       Mary   F    7065  1880\n",
       "1       Anna   F    2604  1880\n",
       "2       Emma   F    2003  1880\n",
       "3  Elizabeth   F    1939  1880\n",
       "4     Minnie   F    1746  1880"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_birth = names.pivot_table('births', index='year',\n",
    "                                columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>90993</td>\n",
       "      <td>110493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>91955</td>\n",
       "      <td>100748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>107851</td>\n",
       "      <td>113687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>112322</td>\n",
       "      <td>104632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>129021</td>\n",
       "      <td>114445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex        F       M\n",
       "year                \n",
       "1880   90993  110493\n",
       "1881   91955  100748\n",
       "1882  107851  113687\n",
       "1883  112322  104632\n",
       "1884  129021  114445"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_birth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e978cf8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74b61ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_birth.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 插入prop列，存放指定的婴儿比例\n",
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>794</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690754</th>\n",
       "      <td>Zaviyon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690755</th>\n",
       "      <td>Zaybrien</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690756</th>\n",
       "      <td>Zayshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690757</th>\n",
       "      <td>Zayyan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690758</th>\n",
       "      <td>Zeal</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690759</th>\n",
       "      <td>Zealan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690760</th>\n",
       "      <td>Zecharia</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690761</th>\n",
       "      <td>Zeferino</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690762</th>\n",
       "      <td>Zekariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690763</th>\n",
       "      <td>Zeki</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690764</th>\n",
       "      <td>Zeriah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690765</th>\n",
       "      <td>Zeshan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690766</th>\n",
       "      <td>Zhyier</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690767</th>\n",
       "      <td>Zildjian</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690768</th>\n",
       "      <td>Zinn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690769</th>\n",
       "      <td>Zishan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690770</th>\n",
       "      <td>Ziven</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690771</th>\n",
       "      <td>Zmari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690772</th>\n",
       "      <td>Zoren</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690773</th>\n",
       "      <td>Zuhaib</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690774</th>\n",
       "      <td>Zyeire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690775</th>\n",
       "      <td>Zygmunt</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690776</th>\n",
       "      <td>Zykerion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690777</th>\n",
       "      <td>Zylar</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690778</th>\n",
       "      <td>Zylin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "5         Margaret   F    1578  1880  0.017342\n",
       "6              Ida   F    1472  1880  0.016177\n",
       "7            Alice   F    1414  1880  0.015540\n",
       "8           Bertha   F    1320  1880  0.014507\n",
       "9            Sarah   F    1288  1880  0.014155\n",
       "10           Annie   F    1258  1880  0.013825\n",
       "11           Clara   F    1226  1880  0.013474\n",
       "12            Ella   F    1156  1880  0.012704\n",
       "13        Florence   F    1063  1880  0.011682\n",
       "14            Cora   F    1045  1880  0.011484\n",
       "15          Martha   F    1040  1880  0.011429\n",
       "16           Laura   F    1012  1880  0.011122\n",
       "17          Nellie   F     995  1880  0.010935\n",
       "18           Grace   F     982  1880  0.010792\n",
       "19          Carrie   F     949  1880  0.010429\n",
       "20           Maude   F     858  1880  0.009429\n",
       "21           Mabel   F     808  1880  0.008880\n",
       "22          Bessie   F     794  1880  0.008726\n",
       "23          Jennie   F     793  1880  0.008715\n",
       "24        Gertrude   F     787  1880  0.008649\n",
       "25           Julia   F     783  1880  0.008605\n",
       "26          Hattie   F     769  1880  0.008451\n",
       "27           Edith   F     768  1880  0.008440\n",
       "28          Mattie   F     704  1880  0.007737\n",
       "29            Rose   F     700  1880  0.007693\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690754    Zaviyon   M       5  2010  0.000003\n",
       "1690755   Zaybrien   M       5  2010  0.000003\n",
       "1690756   Zayshawn   M       5  2010  0.000003\n",
       "1690757     Zayyan   M       5  2010  0.000003\n",
       "1690758       Zeal   M       5  2010  0.000003\n",
       "1690759     Zealan   M       5  2010  0.000003\n",
       "1690760   Zecharia   M       5  2010  0.000003\n",
       "1690761   Zeferino   M       5  2010  0.000003\n",
       "1690762   Zekariah   M       5  2010  0.000003\n",
       "1690763       Zeki   M       5  2010  0.000003\n",
       "1690764     Zeriah   M       5  2010  0.000003\n",
       "1690765     Zeshan   M       5  2010  0.000003\n",
       "1690766     Zhyier   M       5  2010  0.000003\n",
       "1690767   Zildjian   M       5  2010  0.000003\n",
       "1690768       Zinn   M       5  2010  0.000003\n",
       "1690769     Zishan   M       5  2010  0.000003\n",
       "1690770      Ziven   M       5  2010  0.000003\n",
       "1690771      Zmari   M       5  2010  0.000003\n",
       "1690772      Zoren   M       5  2010  0.000003\n",
       "1690773     Zuhaib   M       5  2010  0.000003\n",
       "1690774     Zyeire   M       5  2010  0.000003\n",
       "1690775    Zygmunt   M       5  2010  0.000003\n",
       "1690776   Zykerion   M       5  2010  0.000003\n",
       "1690777      Zylar   M       5  2010  0.000003\n",
       "1690778      Zylin   M       5  2010  0.000003\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "      M      1.0\n",
       "1883  F      1.0\n",
       "      M      1.0\n",
       "1884  F      1.0\n",
       "      M      1.0\n",
       "1885  F      1.0\n",
       "      M      1.0\n",
       "1886  F      1.0\n",
       "      M      1.0\n",
       "1887  F      1.0\n",
       "      M      1.0\n",
       "1888  F      1.0\n",
       "      M      1.0\n",
       "1889  F      1.0\n",
       "      M      1.0\n",
       "1890  F      1.0\n",
       "      M      1.0\n",
       "1891  F      1.0\n",
       "      M      1.0\n",
       "1892  F      1.0\n",
       "      M      1.0\n",
       "1893  F      1.0\n",
       "      M      1.0\n",
       "1894  F      1.0\n",
       "      M      1.0\n",
       "            ... \n",
       "1996  F      1.0\n",
       "      M      1.0\n",
       "1997  F      1.0\n",
       "      M      1.0\n",
       "1998  F      1.0\n",
       "      M      1.0\n",
       "1999  F      1.0\n",
       "      M      1.0\n",
       "2000  F      1.0\n",
       "      M      1.0\n",
       "2001  F      1.0\n",
       "      M      1.0\n",
       "2002  F      1.0\n",
       "      M      1.0\n",
       "2003  F      1.0\n",
       "      M      1.0\n",
       "2004  F      1.0\n",
       "      M      1.0\n",
       "2005  F      1.0\n",
       "      M      1.0\n",
       "2006  F      1.0\n",
       "      M      1.0\n",
       "2007  F      1.0\n",
       "      M      1.0\n",
       "2008  F      1.0\n",
       "      M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 验证分组的总和是否为1\n",
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 求解每对sex/year组合的前1000个名字\n",
    "def get_top_1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">F</th>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>794</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">M</th>\n",
       "      <th>1677617</th>\n",
       "      <td>Yair</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677616</th>\n",
       "      <td>Talan</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677614</th>\n",
       "      <td>Keyon</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677613</th>\n",
       "      <td>Kael</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677618</th>\n",
       "      <td>Demarion</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677619</th>\n",
       "      <td>Gibson</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677620</th>\n",
       "      <td>Reagan</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677621</th>\n",
       "      <td>Cristofer</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677622</th>\n",
       "      <td>Daylen</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677623</th>\n",
       "      <td>Jordon</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677624</th>\n",
       "      <td>Dashawn</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677625</th>\n",
       "      <td>Masen</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677629</th>\n",
       "      <td>Rowen</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677631</th>\n",
       "      <td>Yousef</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677630</th>\n",
       "      <td>Thaddeus</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677628</th>\n",
       "      <td>Kadin</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677627</th>\n",
       "      <td>Dillan</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677626</th>\n",
       "      <td>Clarence</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677634</th>\n",
       "      <td>Slade</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677632</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677633</th>\n",
       "      <td>Sheldon</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677636</th>\n",
       "      <td>Keshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677637</th>\n",
       "      <td>Menachem</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677635</th>\n",
       "      <td>Joziah</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677638</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677639</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677640</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677641</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677642</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677645</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name sex  births  year      prop\n",
       "year sex                                               \n",
       "1880 F   0             Mary   F    7065  1880  0.077643\n",
       "         1             Anna   F    2604  1880  0.028618\n",
       "         2             Emma   F    2003  1880  0.022013\n",
       "         3        Elizabeth   F    1939  1880  0.021309\n",
       "         4           Minnie   F    1746  1880  0.019188\n",
       "         5         Margaret   F    1578  1880  0.017342\n",
       "         6              Ida   F    1472  1880  0.016177\n",
       "         7            Alice   F    1414  1880  0.015540\n",
       "         8           Bertha   F    1320  1880  0.014507\n",
       "         9            Sarah   F    1288  1880  0.014155\n",
       "         10           Annie   F    1258  1880  0.013825\n",
       "         11           Clara   F    1226  1880  0.013474\n",
       "         12            Ella   F    1156  1880  0.012704\n",
       "         13        Florence   F    1063  1880  0.011682\n",
       "         14            Cora   F    1045  1880  0.011484\n",
       "         15          Martha   F    1040  1880  0.011429\n",
       "         16           Laura   F    1012  1880  0.011122\n",
       "         17          Nellie   F     995  1880  0.010935\n",
       "         18           Grace   F     982  1880  0.010792\n",
       "         19          Carrie   F     949  1880  0.010429\n",
       "         20           Maude   F     858  1880  0.009429\n",
       "         21           Mabel   F     808  1880  0.008880\n",
       "         22          Bessie   F     794  1880  0.008726\n",
       "         23          Jennie   F     793  1880  0.008715\n",
       "         24        Gertrude   F     787  1880  0.008649\n",
       "         25           Julia   F     783  1880  0.008605\n",
       "         26          Hattie   F     769  1880  0.008451\n",
       "         27           Edith   F     768  1880  0.008440\n",
       "         28          Mattie   F     704  1880  0.007737\n",
       "         29            Rose   F     700  1880  0.007693\n",
       "...                     ...  ..     ...   ...       ...\n",
       "2010 M   1677617       Yair   M     201  2010  0.000106\n",
       "         1677616      Talan   M     201  2010  0.000106\n",
       "         1677614      Keyon   M     201  2010  0.000106\n",
       "         1677613       Kael   M     201  2010  0.000106\n",
       "         1677618   Demarion   M     200  2010  0.000105\n",
       "         1677619     Gibson   M     200  2010  0.000105\n",
       "         1677620     Reagan   M     200  2010  0.000105\n",
       "         1677621  Cristofer   M     199  2010  0.000105\n",
       "         1677622     Daylen   M     199  2010  0.000105\n",
       "         1677623     Jordon   M     199  2010  0.000105\n",
       "         1677624    Dashawn   M     198  2010  0.000104\n",
       "         1677625      Masen   M     198  2010  0.000104\n",
       "         1677629      Rowen   M     197  2010  0.000104\n",
       "         1677631     Yousef   M     197  2010  0.000104\n",
       "         1677630   Thaddeus   M     197  2010  0.000104\n",
       "         1677628      Kadin   M     197  2010  0.000104\n",
       "         1677627     Dillan   M     197  2010  0.000104\n",
       "         1677626   Clarence   M     197  2010  0.000104\n",
       "         1677634      Slade   M     196  2010  0.000103\n",
       "         1677632    Clinton   M     196  2010  0.000103\n",
       "         1677633    Sheldon   M     196  2010  0.000103\n",
       "         1677636    Keshawn   M     195  2010  0.000103\n",
       "         1677637   Menachem   M     195  2010  0.000103\n",
       "         1677635     Joziah   M     195  2010  0.000103\n",
       "         1677638     Bailey   M     194  2010  0.000102\n",
       "         1677639     Camilo   M     194  2010  0.000102\n",
       "         1677640     Destin   M     194  2010  0.000102\n",
       "         1677641     Jaquan   M     194  2010  0.000102\n",
       "         1677642     Jaydan   M     194  2010  0.000102\n",
       "         1677645     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top_1000)\n",
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "top1000.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261877"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分析命名趋势\n",
    "boys = top1000[top1000.sex=='M']\n",
    "girls = top1000[top1000.sex=='F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_birth = top1000.pivot_table('births', index='year', \n",
    "                                  columns='name', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_birth.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f71eb2f98>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6f7f8d68>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6f7acfd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6f768358>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71eb2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每年叫做John和Mary的婴儿数\n",
    "subset = total_birth[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "subset.plot(subplots=True, figsize=(12, 10),\n",
    "            grid=False,\n",
    "            title='Number of births per year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估命名多样性的增长\n",
    "# 计算最流行的1000个名字所占的比例\n",
    "table = top1000.pivot_table('prop', index='year',\n",
    "                            columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71eb2400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f727198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "           yticks=np.linspace(0, 1.2, 13),\n",
    "           xticks=np.linspace(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2010年男孩中出生前50人数的不同名字的数量\n",
    "df = boys[boys.year==2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先对prop进行排序，然后利用numpy找到0.5的位置\n",
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 117个名字就占据了50%的人数，同理可以分析1900年\n",
    "df = boys[boys.year==1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.values.searchsorted(0.5)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对所有的year/sex进行上述计算\n",
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    return group.prop.cumsum().values.searchsorted(q) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      38\n",
       "      M      14\n",
       "1881  F      38\n",
       "      M      14\n",
       "1882  F      38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ac9d630>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ac96cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title='Number of popular names in top 50%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# “最后一个字母”的变革，发现名字最后一个字母发生了显著变化。\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letter = names.name.map(get_last_letter)\n",
    "last_letter.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letter,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>31446.0</td>\n",
       "      <td>31581.0</td>\n",
       "      <td>36536.0</td>\n",
       "      <td>38330.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>45408.0</td>\n",
       "      <td>49100.0</td>\n",
       "      <td>48942.0</td>\n",
       "      <td>59442.0</td>\n",
       "      <td>58631.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39124.0</td>\n",
       "      <td>38815.0</td>\n",
       "      <td>37825.0</td>\n",
       "      <td>38650.0</td>\n",
       "      <td>36838.0</td>\n",
       "      <td>36156.0</td>\n",
       "      <td>34654.0</td>\n",
       "      <td>32901.0</td>\n",
       "      <td>31430.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50950.0</td>\n",
       "      <td>49284.0</td>\n",
       "      <td>48065.0</td>\n",
       "      <td>45914.0</td>\n",
       "      <td>43144.0</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>42123.0</td>\n",
       "      <td>39945.0</td>\n",
       "      <td>38862.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27113.0</td>\n",
       "      <td>27238.0</td>\n",
       "      <td>27697.0</td>\n",
       "      <td>26778.0</td>\n",
       "      <td>26078.0</td>\n",
       "      <td>26635.0</td>\n",
       "      <td>26864.0</td>\n",
       "      <td>25318.0</td>\n",
       "      <td>24048.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>609.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60838.0</td>\n",
       "      <td>55829.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>51754.0</td>\n",
       "      <td>50670.0</td>\n",
       "      <td>51410.0</td>\n",
       "      <td>50595.0</td>\n",
       "      <td>47910.0</td>\n",
       "      <td>46172.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>33378.0</td>\n",
       "      <td>34080.0</td>\n",
       "      <td>40399.0</td>\n",
       "      <td>41914.0</td>\n",
       "      <td>48089.0</td>\n",
       "      <td>49616.0</td>\n",
       "      <td>53884.0</td>\n",
       "      <td>54353.0</td>\n",
       "      <td>66750.0</td>\n",
       "      <td>66663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>145395.0</td>\n",
       "      <td>144651.0</td>\n",
       "      <td>144769.0</td>\n",
       "      <td>142098.0</td>\n",
       "      <td>141123.0</td>\n",
       "      <td>142999.0</td>\n",
       "      <td>143698.0</td>\n",
       "      <td>140966.0</td>\n",
       "      <td>135496.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                F                                                        \\\n",
       "year            1880     1881     1882     1883     1884     1885     1886   \n",
       "last_letter                                                                  \n",
       "a            31446.0  31581.0  36536.0  38330.0  43680.0  45408.0  49100.0   \n",
       "b                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "c                NaN      NaN      5.0      5.0      NaN      NaN      NaN   \n",
       "d              609.0    607.0    734.0    810.0    916.0    862.0   1007.0   \n",
       "e            33378.0  34080.0  40399.0  41914.0  48089.0  49616.0  53884.0   \n",
       "\n",
       "sex                                       ...            M            \\\n",
       "year            1887     1888     1889    ...         2001      2002   \n",
       "last_letter                               ...                          \n",
       "a            48942.0  59442.0  58631.0    ...      39124.0   38815.0   \n",
       "b                NaN      NaN      NaN    ...      50950.0   49284.0   \n",
       "c                NaN      NaN      NaN    ...      27113.0   27238.0   \n",
       "d             1027.0   1298.0   1374.0    ...      60838.0   55829.0   \n",
       "e            54353.0  66750.0  66663.0    ...     145395.0  144651.0   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2003      2004      2005      2006      2007      2008   \n",
       "last_letter                                                               \n",
       "a             37825.0   38650.0   36838.0   36156.0   34654.0   32901.0   \n",
       "b             48065.0   45914.0   43144.0   42600.0   42123.0   39945.0   \n",
       "c             27697.0   26778.0   26078.0   26635.0   26864.0   25318.0   \n",
       "d             53391.0   51754.0   50670.0   51410.0   50595.0   47910.0   \n",
       "e            144769.0  142098.0  141123.0  142999.0  143698.0  140966.0   \n",
       "\n",
       "sex                              \n",
       "year             2009      2010  \n",
       "last_letter                      \n",
       "a             31430.0   28438.0  \n",
       "b             38862.0   38859.0  \n",
       "c             24048.0   23125.0  \n",
       "d             46172.0   44398.0  \n",
       "e            135496.0  129012.0  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a8cc898>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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469ev56tf/Spz5swBYM6cOVtC1gEHHMCxxx7L7rvvvlU/P/jBDzjssMM49NBDGT9+PLNnz+arX/1qx+s1dEmSpJ63du1abr31Vo4//nh+/vOfc+CBBwKNYPbU8xi3Zf369RxyyCFb1vv6+li/fn3HazR0SZKknvbII4/wxje+kX/4h3/g+c9//g4fP9QjEZN0orStGLokSVLP+s1vfsMb3/hG/uzP/ow3vOENALzwhS/kvvvuA+C+++7jgAMOeMY++vr6WLdu3Zb1gYEBDjrooI7XauiSJEk9qao499xzOfzww7nwwgu3bD/zzDO58sorAbjyyiuZNWvWM/Zz7LHHcvfdd/OTn/yEJ554gkWLFnHmmWd2vF7vXpQkST3pX//1X/nc5z7H9OnTOeqoowD4m7/5Gy666CL+5E/+hE9/+tNMmjSJq666CoB///d/p7+/n40bN/Kc5zyHf/iHf2DVqlU8//nP5x//8R859dRT2bx5M29/+9s54ogjOl6voUuSJA1bO1M8dNqrX/3qIa/HArjxxhuftu13f/d3GRgYGLL96aefzumnn97R+gbz9KIkSVIXtBW6ksxMcleS1UkuGmL/hUlWJbk9yY1JXtSyb3OS25pfiztZvCRJUq/Y7unFJOOA+cAMYABYmmRxVa1qaXYr0F9Vv07yTuBy4C3NfY9W1VEdrluSJKmntDPSdRywuqrWVNUTwCJgq9sAqupbVfXr5urNQF9ny5QkSTubbV1PtSvqxHttJ3QdDKxrWR9obtuWc4Gvt6xPSLIsyc1JXv8sapQkSTuZCRMmsGHDhjERvKqKDRs2MGHChGH1087di0NNyTrkdzjJW4F+4KSWzZOq6t4khwLfTLKiqu4ZdNx5wHkAkyZNaqtwSZI0evr6+hgYGOCBBx4Y7VK6YsKECfT1De9EXjuhawA4pGW9D7h3cKMkJwPvA06qqsef2l5V9zb/XJPkJuBoYKvQVVULgAUA/f39u35kliSpx+2+++5MmTJltMvoKe2cXlwKTE0yJcl4YDaw1V2ISY4GPgWcWVX3t2zfJ8kezeX9gd8DWi/AlyRJGhO2O9JVVZuSzAWuA8YBC6tqZZJ5wLKqWgxcAewJXNV8QOTPqupM4HDgU0mepBHwLht016MkSdKY0NaM9FW1BFgyaNvFLcsnb+O47wHTh1OgJEnSrsAZ6SVJkrrA0CVJktQFhi5JkqQuMHRJkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuMHRJkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrqgrdCVZGaSu5KsTnLREPsvTLIqye1JbkzyopZ9c5Lc3fya08niJUmSesV2Q1eSccB84DRgGnBWkmmDmt0K9FfVy4EvA5c3j90X+ABwPHAc8IEk+3SufEmSpN7QzkjXccDqqlpTVU8Ai4BZrQ2q6ltV9evm6s1AX3P5VOCGqnqwqh4CbgBmdqZ0SZKk3tFO6DoYWNeyPtDcti3nAl9/lsdKkiTtknZro02G2FZDNkzeCvQDJ+3IsUnOA84DmDRpUhslSZIk9ZZ2RroGgENa1vuAewc3SnIy8D7gzKp6fEeOraoFVdVfVf0TJ05st3ZJkqSe0U7oWgpMTTIlyXhgNrC4tUGSo4FP0Qhc97fsug44Jck+zQvoT2lukyRJGlO2e3qxqjYlmUsjLI0DFlbVyiTzgGVVtRi4AtgTuCoJwM+q6syqejDJpTSCG8C8qnpwRN6JJEnSTqyda7qoqiXAkkHbLm5ZPvkZjl0ILHy2BUqSJO0KnJFekiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuMHRJkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQu2G20C5AkSdphl+w9aP3h0aljBzjSJUmS1AVtha4kM5PclWR1kouG2H9ikluSbErypkH7Nie5rfm1uFOFS5Ik9ZLtnl5MMg6YD8wABoClSRZX1aqWZj8DzgHeM0QXj1bVUR2oVZIkqWe1c03XccDqqloDkGQRMAvYErqqam1z35MjUKMkSVLPayd0HQysa1kfAI7fgdeYkGQZsAm4rKqu2YFjJal7Bl+YO2XS6NQhaZfUTujKENtqB15jUlXdm+RQ4JtJVlTVPVu9QHIecB7ApEl+yEmSpF1PO6FrADikZb0PuLfdF6iqe5t/rklyE3A0cM+gNguABQD9/f07Eug0TJMv+tqW5bWXnTGKlUiStGtr5+7FpcDUJFOSjAdmA23dhZhknyR7NJf3B36PlmvBJEmSxortjnRV1aYkc4HrgHHAwqpamWQesKyqFic5FvgKsA/wuiQfrKojgMOBTzUvsH8OjWu6DF2SdhpbjfZOGMVCJO3y2pqRvqqWAEsGbbu4ZXkpjdOOg4/7HjB9mDVKkiT1PGeklyRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuaOvuRUmSRlvr9B7ghM7qPY50SZIkdYGhS5IkqQsMXZIkSV3gNV296pK9W5YfHr06JElSWxzpkiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFXkjfI542KeCEUSpEkiQ9K450SZIkdYGhS5IkqQvaCl1JZia5K8nqJBcNsf/EJLck2ZTkTYP2zUlyd/NrTqcKlyRJ6iXbvaYryThgPjADGACWJllcVatamv0MOAd4z6Bj9wU+APQDBSxvHvtQZ8qXtNNrncgXnMxX0pjVzoX0xwGrq2oNQJJFwCxgS+iqqrXNfU8OOvZU4IaqerC5/wZgJvAvw65ckjS2+WQO9Zh2Ti8eDKxrWR9obmvHcI6VJEnaZbQTujLEtmqz/7aOTXJekmVJlj3wwANtdi1JktQ72gldA8AhLet9wL1t9t/WsVW1oKr6q6p/4sSJbXYtSZLUO9oJXUuBqUmmJBkPzAYWt9n/dcApSfZJsg9wSnObJEnSmLLd0FVVm4C5NMLSncCXqmplknlJzgRIcmySAeDNwKeSrGwe+yBwKY3gthSY99RF9ZIkSWNJW48BqqolwJJB2y5uWV5K49ThUMcuBBYOo0ZJkqSe54z0kiRJXeADryVJXTX9yulbllfMWTGKlUjd5UiXJElSFzjSpd/ycS2SJI0YR7okSZK6wNAlSZLUBYYuSZKkLvCaLkmS1BMmX/S1LctrJ4xiIc+SI12SJEld4EiXJGlM22r05LIzRrES7eoMXZK6qnViTHByTEljh6FL2+Ss0ZIkdY7XdEmSJHWBI12SOq7X7zDS8HX7OqmOnbb2yRwaQY50SZIkdYGhS5IkqQsMXZIkSV3Q1jVdSWYCHwPGAf9cVZcN2r8H8FngGGAD8JaqWptkMnAncFez6c1VdX5nSpc0lrReIwTOpyRpa70wHc12Q1eSccB8YAYwACxNsriqVrU0Oxd4qKoOSzIb+Ajwlua+e6rqqA7XLUmS1FPaOb14HLC6qtZU1RPAImDWoDazgCuby18GXpsknStTkiSpt7VzevFgYF3L+gBw/LbaVNWmJA8D+zX3TUlyK7AReH9V/a/hlSx1Ry8MVUuSekc7oWuoEatqs819wKSq2pDkGOCaJEdU1catDk7OA84DmDRpUhslSRoRzlEkSSOmndOLA8AhLet9wL3bapNkN2Bv4MGqeryqNgBU1XLgHuAlg1+gqhZUVX9V9U+cOHHH34UkSdJOrp2RrqXA1CRTgPXAbOBPB7VZDMwBvg+8CfhmVVWSiTTC1+YkhwJTgTUdq14aJu+IkyR1y3ZDV/MarbnAdTSmjFhYVSuTzAOWVdVi4NPA55KsBh6kEcwATgTmJdkEbAbOr6oHR+KNSJIk7czamqerqpYASwZtu7hl+THgzUMcdzVw9TBrlKRdhjdoSGOXM9JLkiR1QVsjXdKY0Xr33hTvpJUkdY6hSxrDnnYjwYRRKkSSxgBDl6Te5JxiknqM13RJkiR1gSNdkqSRNXhU0uslNUY50iVJktQFhi5JkqQu8PSipF1C66SjTjgqaWdk6NoFOMN1bzEcSL3D31d1kqFrGwwykiSpkwxdkrbJ/+VLUucYuiSpw5420/9lZ4xSJdLweNans3aJ0OUPhSRJ2tntEqGrl3i6RhqDfJC6JJynS5IkqSsc6VJv8mHHktSzxupZn7ZCV5KZwMeAccA/V9Vlg/bvAXwWOAbYALylqtY2970XOBfYDFxQVdd1rPpe4DPHJEk7safd+DHhT3+74r9ZHbXd0JVkHDAfmAEMAEuTLK6qVS3NzgUeqqrDkswGPgK8Jck0YDZwBHAQ8I0kL6mqzTtaaOsPhXcC9ZjW4OmIlNQZjvZKPaedka7jgNVVtQYgySJgFtAaumYBlzSXvwz8Y5I0ty+qqseBnyRZ3ezv+8OqeicfPdoqIE4YxUIk7VL8bFGvcNqUobUTug4G1rWsDwDHb6tNVW1K8jCwX3P7zYOOPfhZV9thDqmOjKd/X3+7PJzpPZ7pH5yd7fqArWv90613jsDPltOmaGf7HdC2jdTv6079M7CTD5Y8k05+X1NVz9wgeTNwalW9o7n+H4Hjquo/t7RZ2Wwz0Fy/h8aI1jzg+1X1+eb2TwNLqurqQa9xHnBec/WlwF1t1r8/8Is22+6Ikeh3rNc61t//SPVrrdY61msd6+9/pPq11vb7fVFVTWynw3ZGugaAQ1rW+4B7t9FmIMluwN7Ag20eS1UtABa0U3CrJMuqqn9HjxuNfsd6rWP9/Y9Uv9ZqrWO91rH+/keqX2sdmX7bmadrKTA1yZQk42lcGL94UJvFwJzm8puAb1ZjCG0xMDvJHkmmAFOBH3SmdEmSpN6x3ZGu5jVac4HraEwZsbCqViaZByyrqsXAp4HPNS+Uf5BGMKPZ7ks0LrrfBLzr2dy5KEmS1OvamqerqpYASwZtu7hl+THgzds49sPAh4dR4zPZ4VOSo9jvWK91rL//kerXWq11rNc61t//SPVrrSPQ73YvpJckSdLw+exFSZKkLjB0NSWZnOSO0a7j2UhySZL3jHYdzyTJBUnuTPKF0a5lW0byZyDJ90ai3073PcLfg0dGol+pk5K8IMl/Gu06tGsydKlb/hNwelX92WgXMhqq6j/0Yt/SSEjDzvrvzwtofF5JHbez/tBvV5JrkixPsrI5uWon7JbkyiS3J/lykud2otMkZzf7/GGSz3Woz/cluSvJN2hMKNuJPt+a5AdJbkvyqeZzNzvR7yeBQ4HFSf7PDvX510l+lOSGJP/SwZG+cUn+a/Pn6vokv9OJTkdylGek+k5yaJJbkxw7Ev0/W83RuB8l+eckdyT5QpKTk/xrkruTHDfMvu/s9M9Akgubtd6R5C+G219LrT/q9GdW62dLJ3+3Wr63/wTcwtZzOD6b/p6X5GvNz9U7krylE3UClwEvbn4OXtGJDgePICd5T5JLhtnnR1pH5JpnPP6vYfb5fye5oLn890m+2Vx+bZLPD6PfY5s/oxOaf28rkxw5nFqb/V6a5N0t6x9+qv5h9nt+8+//tiQ/SfKt4fa5RVX15Bewb/PP3wHuAPYbZn+TgQJ+r7m+EHhPB+o8gsYM+/u31j3MPo8BVgDPBZ4PrB5urcDhwP8Adm+u/xNwdgf/vtY+9T3oQF/9wG3Nv/u9gLs79Hc1mcbUJkc1178EvLVDNT/Sqe/lSPbd/B7cQSPI3/rU92JnqrPl72k6jf84Lm/+vj71vNdrdqafgZbf1+cBewIrgaM79H3o6GfWSHy2DKr3SeBVHervjcB/bVnfu4N13tGJvrbVJ/Ae4JJh9nk08O2W9VXApGH2+Srgquby/6Ixr+buwAeAPx9m3x8CPgrMB97bwe/rLc3l5wD3MMwsMKj/3Zvfh9d1qs+eHekCLkjyQxrPdjyExsSrw7Wuqv61ufx54NUd6PMPgS9X1S8AqurBDvT5+8BXqurXVbWRp09W+2y8lsYH7tIktzXXD+1AvyPh1cBXq+rRqvoljbDYKT+pqtuay8tp/FKPNROBr9IIG7dtr/Eo+UlVraiqJ2mEmBur8Sm5guH/nXX6Z+DVNH5ff1VVjwD/ncbvcCd0+jNrJD5bWv20qm6vvYgwAAAgAElEQVTefrO2rABObo74/H5VPdyhfntCVd0KHJDkoCSvAB6qqp8Ns9vlwDFJ9gIeB75P4z+5v08jfAzHPGBGs7/Lh9kXAFW1FtiQ5GjgFODWqtrQib6bPkZjsveO/RvT1jxdO5skfwCcDJxQVb9OchMw4RkPas/g+TM6MZ9GOtTPYJ3uM8CVVfXeDvc7EjKCfT/esryZxmjaWPMwjQfY/x6NQLMzav17erJl/UmG/7nW6Z+Bkfx5HYnPrJGcR+hXneqoqn6c5BjgdOD/SXJ9Vc3rVP8dtomtL+fpxL9XAF+m8RSY3wUWDbezqvpNkrXA24DvAbcDrwFeDNw5zO73pTHSuzuN99+pn4V/Bs6h8T1Y2KE+SXIO8CJgbqf6hN69pmtvGqn+10leRmNItBMmJTmhuXwW8N0O9Hkj8CdJ9gNIsm8H+vwO8MdJfqf5P5LXdaDPG4E3JTkAGnUmeVEH+h0J3wVe17w+YE/gjNEuaBfzBPB64OwkfzraxewCvgO8PslzkzwP+GOGP2rwlE5/Zo3EZ8uISHIQ8Ouq+jyN01av7FDXv6Rx2UIn/ZzGqNR+SfYA/qhD/S6i8QSYN9EIYJ3wHRqnP79D4+f0fOC25kjycCwA/hr4AvCRYfbV6ivATOBYGk/OGbZmmH8PjdH+JzvR51N6cqQLuBY4P8ntNK6X6tRw9Z3AnCSfonGd0CeG22E1HoX0YeDbSTbTuE7mnGH2eUuS/0bjuqaf0oEP8KpaleT9wPVp3FX0G+Bdzf53KlW1NMli4Ic06ltGY3RmrOr4yERV/SrJHwE3JPlVVX21068xVjR/Xz/Db587+8/NU0Od0NHPrJH4bBlB04ErkjxJ4/PqnZ3otKo2NG/KuAP4elX9ZQf6/E0aj877N+AnwI+G22ez35XNcLy+qu7rRJ80/s7fB3y/+TnwGMP8OUhyNrCpqr6Yxg1a30vyh1X1zeEWW1VPNC90/9/VuccMzqUxMvetJNB45OE7OtGxM9KrJyXZs6oead6t9R3gvKq6ZbTr6rbmCOotVbWzjkpqhCSZDPzPqhr2XWDP8BqX0LgB4qMj9RrScDQHCW4B3lxVd492PdvTq6cXpQXNC/5vAa4eo4HrIBoXuvoPoqQxJ8k0GnfY3tgLgQsc6ZIkSeoKR7okSZK6wNAlSZLUBYYuSZKkLjB0SZIkdYGhS9JOIc/ywd1J/mJ7D3pOsjbJ/ttp819all/Q+jBhSeoEQ5ekXvcXNB7QPFz/pWX5BcAlST7f7sFp8DNV0jb5ASFpp5JkzyQ3Jnk8yZNJHk3ySPPrG0l+mOSOJG9JcgFwEI2Zo7/VZv9vTfKDJLcl+VSScUkuA36nue0LwGXAPsDpSa5oHveXSZYmuT3JB5vbJie5M8k/0Zgz7pCR+J5I2jU4T5eknUKSR6pqzyS70Ri5uh24ELgcmAq8AZhZVf9Hs/3eVfVw8wG9/VX1i2foey3QD0xs9veG5qNZ/gm4uao++9TrN9tPpvHYnuur6q1JTqHxfLs/p/EA68XNfn4GrAH+Q1V16nFkknZRjnRJ2tkE+BsaI1iXAwcDLwRWACcn+VySFcBPk/wQmLDlwOSmJB9K8r3myNj/aD4qaX8az7y7FjgOWNp8osFZwP+bZCONka7f30ZNp9B4+PMjNJ7zN4PfPrT4pwYuSe0wdEna2fwZjRGpe4HzgZ8DE6rqxzSCzutpfHb9PfCeZtv9Wo6fDfxHGmHtxTQelfQIcFizr59X1VFVdRTwn4FDaTzcdhNwVZIJPN1eza83A7s36zi3edyvOvXGJe3aDF2SdjZ7A/c3l/8H8CLg9iTX0ghc1wDvA46uqhuAX/PbUSeA/6+q7qmqh4GvA/cAjwGbgc8AL01yQLPtEmDPqtrU7GcP4KXAL4HxLX2Oa/bxnap6ElhF4/Tnazr4viXt4nYb7QIkaZAv0AhbBwI3AlOA02iEoS/QGF06C3g0yf+mEY7+KslTwevnLX092lw/vLn+Y+Ah4PrmnYb7AJuS7EsjcO0B/G1VnZxkHfBHzQvpn6ARBh9OAlDNbT/o/NuXtKtypEvSTuGpi9ir6hdVdQJwH/B3VXV4Va2tquuAvwU+XVW7VdVeVfWCqtqjqg6oqm2OOlXV5JYL7R9pnlp8F42QdSawT1VNAP43jTsXAa4G/mdV/SWwDvhMVY1rfu1WVc+tqr+qqiNH4NshaRdk6JLUSz4PvC7Jqc2pHiYk+YMkfc+ir71oXMf1ALBbkouB53fhdSWNUYYuST2jqtYBs2hMZPoAjRGov+S3n2WvBC5uzrd1G/AOGhOdDuU6Gtd8/Rj4KY1rttY9y9eVpO1yni5JkqQu8H9pkiRJXWDokiRJ6oK2QleSmUnuSrI6yUXP0O5NSSpJf8u29zaPuyvJqZ0oWpIkqddsd56uJOOA+TQeezFA4/EZi6tq1aB2ewEXAP/Wsm0ajdmhj6DxSI9vJHlJVW3u3FuQJEna+bUz0nUcsLqq1lTVE8AiGnfxDHYpjeekPdaybRawqKoer6qfAKub/UmSJI0p7cxIfzBb30Y9ABzf2iDJ0cAhVfU/k7xn0LE3Dzr24MEvkOQ84DyA5z3vece87GUva696SZKkUbR8+fJfVNXEdtq2E7oyxLYt80w0H6Xx98A5O3rslg1VC4AFAP39/bVs2bI2ypIkSRpdSX7abtt2QtcAcEjLeh9wb8v6XsCRwE3NZ5L9LrA4yZltHCtJkjQmtHNN11JgapIpScbTuDB+8VM7q+rhqtq/+WyzyTROJ55ZVcua7WYn2SPJFGAqPiBWkiSNQdsd6aqqTUnm0nhkxjhgYVWtTDIPWFZVi5/h2JVJvgSsovGMs3d556IkSRqLdrrHAHlNlyRJ6hVJlldV//ZbOiO9JElSVxi6JEmSusDQJUmS1AWGLkmSpC5oZ56und70K6dvtb5izopRqkSSJGlojnRJkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuMHRJkiR1gaFLkiSpC9oKXUlmJrkryeokFw2x//wkK5LcluS7SaY1t09O8mhz+21JPtnpNyBJktQLtvvsxSTjgPnADGAAWJpkcVWtamn2xar6ZLP9mcDfATOb++6pqqM6W7YkSVJvaWek6zhgdVWtqaongEXArNYGVbWxZfV5QHWuREmSpN7XTug6GFjXsj7Q3LaVJO9Kcg9wOXBBy64pSW5N8u0kvz+saiVJknpUO6ErQ2x72khWVc2vqhcDfwW8v7n5PmBSVR0NXAh8Mcnzn/YCyXlJliVZ9sADD7RfvSRJUo9oJ3QNAIe0rPcB9z5D+0XA6wGq6vGq2tBcXg7cA7xk8AFVtaCq+quqf+LEie3WLkmS1DPaCV1LgalJpiQZD8wGFrc2SDK1ZfUM4O7m9onNC/FJcigwFVjTicIlSZJ6yXbvXqyqTUnmAtcB44CFVbUyyTxgWVUtBuYmORn4DfAQMKd5+InAvCSbgM3A+VX14Ei8EUmSpJ3ZdkMXQFUtAZYM2nZxy/K7t3Hc1cDVwylwSJfsvfX6lEkdfwlJkqROckZ6SZKkLjB0SZIkdUFbpxe165p80de2LK+97IxRrESSpF2bI12SJEldYOiSJEnqAkOXJElSFxi6JEmSusDQJUmS1AWGLkmSpC4wdEmSJHWBoUuSJKkLDF2SJEldYOiSJEnqAkOXJElSFxi6JEmSusDQJUmS1AVtha4kM5PclWR1kouG2H9+khVJbkvy3STTWva9t3ncXUlO7WTxkiRJvWK7oSvJOGA+cBowDTirNVQ1fbGqplfVUcDlwN81j50GzAaOAGYC/9TsT5IkaUxpZ6TrOGB1Va2pqieARcCs1gZVtbFl9XlANZdnAYuq6vGq+gmwutmfJEnSmLJbG20OBta1rA8Axw9ulORdwIXAeOAPW469edCxBz+rSiVJknpYOyNdGWJbPW1D1fyqejHwV8D7d+TYJOclWZZk2QMPPNBGSZIkSb2lndA1ABzSst4H3PsM7RcBr9+RY6tqQVX1V1X/xIkT2yhJkiSpt7QTupYCU5NMSTKexoXxi1sbJJnasnoGcHdzeTEwO8keSaYAU4EfDL9sSZKk3rLda7qqalOSucB1wDhgYVWtTDIPWFZVi4G5SU4GfgM8BMxpHrsyyZeAVcAm4F1VtXmE3oskSdJOq50L6amqJcCSQdsubll+9zMc+2Hgw8+2QEmSpF2BM9JLkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuaGty1J3B5Iu+tmV57YRRLESSJOlZcKRLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuaCt0JZmZ5K4kq5NcNMT+C5OsSnJ7khuTvKhl3+YktzW/FneyeEmSpF6x3clRk4wD5gMzgAFgaZLFVbWqpdmtQH9V/TrJO4HLgbc09z1aVUd1uG5JkqSe0s5I13HA6qpaU1VPAIuAWa0NqupbVfXr5urNQF9ny5QkSept7YSug4F1LesDzW3bci7w9Zb1CUmWJbk5yeuHOiDJec02yx544IE2SpIkSeot7Tx7MUNsqyEbJm8F+oGTWjZPqqp7kxwKfDPJiqq6Z6vOqhYACwD6+/uH7FuSJKmXtTPSNQAc0rLeB9w7uFGSk4H3AWdW1eNPba+qe5t/rgFuAo4eRr2SJEk9qZ3QtRSYmmRKkvHAbGCruxCTHA18ikbgur9l+z5J9mgu7w/8HtB6Ab4kSdKYsN3Ti1W1Kclc4DpgHLCwqlYmmQcsq6rFwBXAnsBVSQB+VlVnAocDn0ryJI2Ad9mgux4lSZLGhHau6aKqlgBLBm27uGX55G0c9z1g+nAKlCRJ2hU4I70kSVIXGLokSZK6wNAlSZLUBYYuSZKkLjB0SZIkdYGhS5IkqQsMXZIkSV1g6JIkSeoCQ5ckSVIXGLokSZK6wNAlSZLUBYYuSZKkLmjrgdcaIy7Ze9D6w6NThyRJuyBHuiRJkrrA0CVJktQFbYWuJDOT3JVkdZKLhth/YZJVSW5PcmOSF7Xsm5Pk7ubXnE4WL0mS1Cu2G7qSjAPmA6cB04Czkkwb1OxWoL+qXg58Gbi8eey+wAeA44HjgA8k2adz5UuSJPWGdka6jgNWV9WaqnoCWATMam1QVd+qql83V28G+prLpwI3VNWDVfUQcAMwszOlS5Ik9Y52QtfBwLqW9YHmtm05F/j6szxWkiRpl9TOlBEZYlsN2TB5K9APnLQjxyY5DzgPYNKkSW2UJEmS1FvaGekaAA5pWe8D7h3cKMnJwPuAM6vq8R05tqoWVFV/VfVPnDix3dolSZJ6RjuhaykwNcmUJOOB2cDi1gZJjgY+RSNw3d+y6zrglCT7NC+gP6W5TZIkaUzZ7unFqtqUZC6NsDQOWFhVK5PMA5ZV1WLgCmBP4KokAD+rqjOr6sEkl9IIbgDzqurBEXknkiRJO7G2HgNUVUuAJYO2XdyyfPIzHLsQWPhsC5QkSdoVOCO9JElSFxi6JEmSusDQJUmS1AWGLkmSpC4wdEmSJHWBoUuSJKkL2poyQmPT9Cunb1leMWfFKFYiSVLvc6RLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrA0CVJktQFhi5JkqQuMHRJkiR1gaFLkiSpC9oKXUlmJrkryeokFw2x/8QktyTZlORNg/ZtTnJb82txpwqXJEnqJdt9DFCSccB8YAYwACxNsriqVrU0+xlwDvCeIbp4tKqO6kCtkiRJPaudZy8eB6yuqjUASRYBs4Atoauq1jb3PTkCNUqSJPW8dkLXwcC6lvUB4PgdeI0JSZYBm4DLquqaHThWkiTp6S7Ze9D6w6NTxw5oJ3RliG21A68xqaruTXIo8M0kK6rqnq1eIDkPOA9g0qRJO9C1JElSb2jnQvoB4JCW9T7g3nZfoKrubf65BrgJOHqINguqqr+q+idOnNhu15IkST2jndC1FJiaZEqS8cBsoK27EJPsk2SP5vL+wO/Rci2YJEnSWLHd0FVVm4C5wHXAncCXqmplknlJzgRIcmySAeDNwKeSrGwefjiwLMkPgW/RuKbL0CVJksacdq7poqqWAEsGbbu4ZXkpjdOOg4/7HjB9mDVKkiT1vLZClySNRdOv3Pr/jCvmrBilSiTtCnwMkCRJUhcYuiRJkrrA0CVJktQFXtMlSU8ZPMP1FCdrltQ5jnRJkiR1gaFLkiSpCwxdkiRJXWDokiRJ6gJDlyRJUhcYuiRJkrrAKSMkSVLP64XHdjnSJUmS1AWGLkmSpC7w9KIkSeoJky/62pbltRNGsZBnyZEuSZKkLmgrdCWZmeSuJKuTXDTE/hOT3JJkU5I3Ddo3J8ndza85nSpckiSpl2w3dCUZB8wHTgOmAWclmTao2c+Ac4AvDjp2X+ADwPHAccAHkuwz/LIlSZJ6SzsjXccBq6tqTVU9ASwCZrU2qKq1VXU78OSgY08FbqiqB6vqIeAGYGYH6pYkSeop7YSug4F1LesDzW3tGM6xkiRJu4x2QleG2FZt9t/WsUnOS7IsybIHHnigza4lSZJ6RzuhawA4pGW9D7i3zf7bOraqFlRVf1X1T5w4sc2uJUmSekc7oWspMDXJlCTjgdnA4jb7vw44Jck+zQvoT2lukyRJGlO2G7qqahMwl0ZYuhP4UlWtTDIvyZkASY5NMgC8GfhUkpXNYx8ELqUR3JYC85rbJEmSxpS2ZqSvqiXAkkHbLm5ZXkrj1OFQxy4EFg6jRkmSpJ7njPSSJEldYOiSJEnqAkOXJElSFxi6JEmSusDQJUmS1AVt3b0oSbuqyRd9bcvy2gmjWIikXZ6hS9qG6VdO32p9xZwVo1SJJGlX4OlFSZKkLjB0SZIkdYGhS5IkqQsMXZIkSV1g6JIkSeoCQ5ckSVIXGLokSZK6wNAlSZLUBW2FriQzk9yVZHWSi4bYv0eS/9bc/29JJje3T07yaJLbml+f7Gz5kiRJvWG7M9InGQfMB2YAA8DSJIuralVLs3OBh6rqsCSzgY8Ab2nuu6eqjupw3ZIkST2lnZGu44DVVbWmqp4AFgGzBrWZBVzZXP4y8Nok6VyZkiRJva2dZy8eDKxrWR8Ajt9Wm6ralORhYL/mvilJbgU2Au+vqv81vJKlzml92DHA2svOGKVKJEm7unZC11AjVtVmm/uASVW1IckxwDVJjqiqjVsdnJwHnAcwadKkNkqSJEnqLe2cXhwADmlZ7wPu3VabJLsBewMPVtXjVbUBoKqWA/cALxn8AlW1oKr6q6p/4sSJO/4uJEmSdnLthK6lwNQkU5KMB2YDiwe1WQzMaS6/CfhmVVWSic0L8UlyKDAVWNOZ0iVJknrHdk8vNq/RmgtcB4wDFlbVyiTzgGVVtRj4NPC5JKuBB2kEM4ATgXlJNgGbgfOr6sGReCOSJEk7s3au6aKqlgBLBm27uGX5MeDNQxx3NXD1MGuU1GNab1Dw5gRJamgrdEkahkv2HrT+8OjUIUkaVYYuSVJPcIoX9TpDl9SqdVRqitOXSJI6x9AlaWR5elWSAEOX1HXTr5y+ZXnFnBWjWIkkqZvamadLkiRJw+RIlyRpTHOKE3WLoUtSV7WeXgVPsUoaOzy9KEmS1AWGLkmSpC4wdEmSJHWB13T1CGdilrSrcNoUjVWGLkmSnuJkvhpBhi5JUsd1exoG74pVLzB0SZJ6k89KVY/xQnpJkqQuaGukK8lM4GPAOOCfq+qyQfv3AD4LHANsAN5SVWub+94LnAtsBi6oqus6Vr2ksctrb9QFXvSvTtpu6EoyDpgPzAAGgKVJFlfVqpZm5wIPVdVhSWYDHwHekmQaMBs4AjgI+EaSl1TV5k6/EUnSTmpwQPZUoMaodk4vHgesrqo1VfUEsAiYNajNLODK5vKXgdcmSXP7oqp6vP7/9u492KqyDuP490lJQQzHW42moI5TVk4pWl6bvGSOo5MapnlBa8x7ZIXNeCujTE1nmv6IAi+TCWWmqZiaGJrkBUWORzgI3gKVcnLUUoFUkF9/vO/RzXGAzVnv2ucc9vOZYVhrz9rP+e21137Pe9619nojFgDP5DwzMzOzttLM6cWtgRca1hcBn1vVNhGxXNJrwGb58Rk9nrt1r6u19zT85bhzj78aPQTe91b65taGfVjIOuR996rrsV/702mg99d67LvL/rzaQNIO3wpt5X0wFRGr30A6CvhSRJyc108APhsR32rYZm7eZlFef5Y0ojUOeCgiJuXHrwbuiIibevyMU4BT8urHgCebrH9z4OUmt10bdeS2e63t/vrrynWtrrXda233119XrmttPnd4RGzRTGAzI12LgG0a1j8K/GsV2yyStD4wDHi1yecSEROBic0U3EjSoxGx29o+ry9y273Wdn/9deW6Vtfa7rW2++uvK9e11pPbzDVdM4EdJW0n6YOkC+On9NhmCnBiXh4F3BNpCG0KcIykDSRtB+wIPFKmdDMzM7OBY40jXfkarbOAu0i3jLgmIuZKGgc8GhFTgKuB6yQ9QxrhOiY/d66kG4AngOXAmf7mopmZmbWjpu7TFRF3AHf0eOwHDctvAket4rkXAxdXqHF11vqUZB/mtnut7f7668p1ra613Wtt99dfV65rrSF3jRfSm5mZmVl1ngbIzMzMrAXc6cokjZDU1dd19IakiySN7es6VkfSGEnzJE3u61pWpc5jQNKDdeSWzq55HyyuI9esJEmbSDqjr+uwdZM7XdYqZwCHRMRxfV1IX4iIvQZitlkdlPTX3z+bkNors+L660G/RpJukTRL0tx8c9US1pd0raTZkm6UNKREqKTROfNxSdcVyjxf0pOS/kq6oWyJzOMlPSKpU9KEPO9midxfA9sDUyR9p1DmhZLmS7pb0u8LjvStJ+nKfFxNlTS4RGidozx1ZUvaXtJjknavI7+38mjcfElXSeqSNFnSgZIekPS0pF5PNZaz55U+BiR9N9faJensqnkNtc4v3WY1ti0lP1sN+3Y80MHK93DsTd5Gkm7P7WqXpKNL1AlcCuyQ28HLSwT2HEGWNFbSRRUzL2sckctnPL5XMfP7ksbk5Z9LuicvHyBpUoXc3fMxumF+3+ZK+lSVWnPujyV9u2H94u76K+aelt//TkkLJN1bNfNdETEg/wGb5v8HA13AZhXzRgAB7J3XrwHGFqjzk6Q77G/eWHfFzJHAHGAI8CHSnJaVagV2Am4DBuX18cDogu/Xwu59UCBrN6Azv/cbA08Xeq9GkG5t8pm8fgNwfKGaF5fal3Vm533QRerIP9a9L/pTnQ3v086kPxxn5c9r93yvt/SnY6Dh87oRMBSYC+xSaD8UbbPqaFt61LsC2KNQ3leAKxvWhxWss6tE1qoygbHARRUzdwHua1h/Ati2YuYewB/z8t9J99UcBPwQOLVi9k+AK4BfAucW3K8defkDwLNU7Av0yB+U98NhpTIH7EgXMEbS46S5Hbch3Xi1qhci4oG8PAnYp0Dm/sCNEfEyQES8WiBzX+DmiFgaEa/z/pvV9sYBpAZ3pqTOvL59gdw67APcGhH/i4g3SJ3FUhZERGdenkX6ULebLYBbSZ2NzjVt3EcWRMSciFhB6sRMi9RKzqH6e1b6GNiH9HldEhGLgT+RPsMllG6z6mhbGj0XETPWvFlT5gAH5hGffSPitUK5A0JEPAZsKWkrSZ8G/hMRz1eMnQWMlLQx8BbwEOmP3H1JnY8qxgFfzHk/q5gFQEQsBF6RtAtwEPBYRLxSIjv7Belm78V+xzR1n67+RtIXgAOBPSNiqaS/ASWmFe55/4wS99NQoZyeSmcKuDYizi2cWwfVmP1Ww/I7pNG0dvMaaQL7vUkdmv6o8X1a0bC+gurtWuljoM7jtY42q877CC0pFRQRT0kaCRwCXCJpakSMK5Vf2HJWvpynxO8rgBtJs8B8BLi+alhELJO0EPg68CAwG9gP2AGYVzF+U9JI7yDS6y91LFwFnETaB9cUykTSScBw4KxSmTBwr+kaRurVL5X0cdKQaAnbStozL38NuL9A5jTgq5I2A5C0aYHM6cARkgbnv0gOK5A5DRglaUtIdUoaXiC3DvcDh+XrA4YC9U0J357eBg4HRks6tq+LWQdMBw6XNETSRsARVB816Fa6zaqjbamFpK2ApRExiXTaatdC0W+QLlso6d+kUanNJG0AHFoo93rSDDCjSB2wEqaTTn9OJx2npwGdeSS5ionAhcBk4LKKWY1uBg4GdifNnFNZ7syPJY32ryiR2W1AjnQBfwFOkzSbdL1UqeHqecCJkiaQrhP6VdXASFMhXQzcJ+kd0nUyJ1XM7JD0B9J1Tc9RoAGPiCckXQBMVfpW0TLgzJzfr0TETElTgMdJ9T1KGp1pV8VHJiJiiaRDgbslLYmIW0v/jHaRP6+/4b15Z6/Kp4ZKKNpm1dG21Ghn4HJJK0jt1eklQiPilfyljC7gzog4p0DmMqWp8x4GFgDzq2bm3Lm5c/zPiHixRCbpPT8feCi3A29S8TiQNBpYHhG/U/qC1oOS9o+Ie6oWGxFv5wvd/xvlphk8izQyd68kSFMenlwi2HektwFJ0tCIWJy/rTUdOCUiOvq6rlbLI6gdEdFfRyWtJpJGAH+OiMrfAlvNz7iI9AWIK+r6GWZV5EGCDuCoiHi6r+tZk4F6etFsYr7gvwO4qU07XFuRLnT1L0QzazuSPkH6hu20gdDhAo90mZmZmbWER7rMzMzMWsCdLjMzM7MWcKfLzMzMrAXc6TIzMzNrAXe6zKxfUC8n7pZ09pomepa0UNLma9jmvIblTRonEzYzK8GdLjMb6M4mTdBc1XkNy5sAa9XpUuI21cxWyQ2EmfUrkoZKmiapQ9IcSV/Oj28k6XZJj0vqknS0pDHAVqQ7R9/bZP7xkh6R1ClpgqT1JF0KDM6PTdiywI4AAAH1SURBVAYuBXbI65fn550jaaak2ZJ+lB8bIWmepPGke8ZtU8MuMbN1hO/TZWb9gqTFETFU0vrAkIh4PZ8SnAHsCBwJHBwR38zbD4uI1/IEvbtFxMuryV4I7AZsAfwMODJPzTIemBERv+3++Xn7ETTc7V3SQaT57U4lTWA9Jec8D/wD2CsiSk1HZmbrqIE696KZrbsE/FTS54EVwNbAh4E5wBWSLiN1iHozH9wBwEhgZp5TbTDwUhPPOyj/654zcSipI/g88Jw7XGbWDHe6zKy/OY40IjUyj0YtBDaMiKckjQQOAS6RNDUixq1ltoBrI+LcXjzvkoiYsNKDaURsyVpmmVmb8jVdZtbfDANeyh2u/YDh8O5ck0sjYhJpvsld8/ZvABs3mT0NGCVpy5y5qaTuycKXSRq0isy7gG9I6j79uHV3hplZszzSZWb9zWTgNkmPAp3A/Pz4zsDlklYAy4DT8+MTgTslvRgR+60uOCKekHQBMDV/03AZcCbwXM6ZLakjIo6T9ICkLuDOiDhH0k7AQ/m05GLgeOCdgq/bzNZxvpDezMzMrAV8etHMzMysBXx60czWGZIeBjbo8fAJETGnL+oxM2vk04tmZmZmLeDTi2ZmZmYt4E6XmZmZWQu402VmZmbWAu50mZmZmbWAO11mZmZmLfB/l3kchgSitO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ab84438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female', legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table / table.sum()\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ab84358>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a9394a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 变成女孩名字的男孩名字以及反过来的情况\n",
    "all_names = pd.Series(top1000.name.unique())\n",
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用这个结果过滤其他的名字，并按名字计算出生数以查看相对频率\n",
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.091954</td>\n",
       "      <td>0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.893204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.065693</td>\n",
       "      <td>0.934307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.946970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  0.091954  0.908046\n",
       "1881  0.106796  0.893204\n",
       "1882  0.065693  0.934307\n",
       "1883  0.053030  0.946970\n",
       "1884  0.107143  0.892857"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 然后按照性别和年度进行聚合，并按照年度规范化处理\n",
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ab84390>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a60f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F':'k--'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
